
No hay coincidencias. Prueba con “video”, “FLUX” o “webhooks”.
Cargando modelos…
flux/dev
# Run the model; wait up to 20 s for the finished result in the same call
curl -X POST "https://vit.doitong.com/api/v1/run/flux/dev?wait=20" \
-H "Authorization: Bearer $DOITONG_API_KEY" \
-H "Content-Type: application/json" \
-d '{
"prompt": "A lighthouse keeper'\''s cottage on a windswept cliff at blue hour, warm light glowing from small square windows, waves breaking far below, long-exposure sea mist, moody cinematic landscape photograph, 35mm, rich detail.",
"num_outputs": 1
}'
# Still "queued" or "running"? Poll with the id from the response:
curl "https://vit.doitong.com/api/v1/requests/REQUEST_ID?wait=20" \
-H "Authorization: Bearer $DOITONG_API_KEY"
const API = "https://vit.doitong.com/api/v1";
const headers = {
"Authorization": "Bearer " + process.env.DOITONG_API_KEY,
"Content-Type": "application/json",
};
// Run the model; wait up to 20 s for the finished result in the same call
let res = await fetch(API + "/run/flux/dev?wait=20", {
method: "POST",
headers,
body: JSON.stringify({
"prompt": "A lighthouse keeper's cottage on a windswept cliff at blue hour, warm light glowing from small square windows, waves breaking far below, long-exposure sea mist, moody cinematic landscape photograph, 35mm, rich detail.",
"num_outputs": 1
}),
});
let request = await res.json();
if (!res.ok) throw new Error(request.error.code + ": " + request.error.message);
// Not finished yet: long-poll until it is
while (request.status === "queued" || request.status === "running") {
res = await fetch(API + "/requests/" + request.id + "?wait=20", { headers });
request = await res.json();
}
if (request.status !== "succeeded") throw new Error(JSON.stringify(request.error));
console.log(request.outputs.map((o) => o.url), request.cost.credits + " API credits ($" + request.cost.usd + ")");
import os
import requests
API = "https://vit.doitong.com/api/v1"
headers = {"Authorization": "Bearer " + os.environ["DOITONG_API_KEY"]}
payload = {
"prompt": "A lighthouse keeper's cottage on a windswept cliff at blue hour, warm light glowing from small square windows, waves breaking far below, long-exposure sea mist, moody cinematic landscape photograph, 35mm, rich detail.",
"num_outputs": 1,
}
# Run the model; wait up to 20 s for the finished result in the same call
r = requests.post(API + "/run/flux/dev", params={"wait": 20}, json=payload, headers=headers)
request = r.json()
if not r.ok:
raise RuntimeError(request["error"]["code"] + ": " + request["error"]["message"])
# Not finished yet: long-poll until it is
while request["status"] in ("queued", "running"):
request = requests.get(API + "/requests/" + request["id"], params={"wait": 20}, headers=headers).json()
if request["status"] != "succeeded":
raise RuntimeError(request["error"])
print([o["url"] for o in request["outputs"]], request["cost"]["credits"], "API credits", "$" + request["cost"]["usd"])
# 1. Connect once (Claude Code; Cursor, VS Code, Claude Desktop: https://doitong.com/es-MX/developers/docs/mcp)
claude mcp add --transport http doitong-models https://vit.doitong.com/api/v1/mcp \
--header "Authorization: Bearer $DOITONG_API_KEY"
# 2. Your agent calls the "run" tool with:
{
"model_id": "flux/dev",
"input": {
"prompt": "A lighthouse keeper's cottage on a windswept cliff at blue hour, warm light glowing from small square windows, waves breaking far below, long-exposure sea mist, moody cinematic landscape photograph, 35mm, rich detail.",
"num_outputs": 1
}
}
Llama al modelo FLUX.1 [dev] (flux/dev) mediante Doitong API. Documentación para agentes: https://doitong.com/es-MX/developers/models/flux/dev.md
POST https://vit.doitong.com/api/v1/run/flux/dev?wait=20
Authorization: Bearer $DOITONG_API_KEY
Content-Type: application/json
{
"prompt": "A lighthouse keeper's cottage on a windswept cliff at blue hour, warm light glowing from small square windows, waves breaking far below, long-exposure sea mist, moody cinematic landscape photograph, 35mm, rich detail.",
"num_outputs": 1
}
Si el estado de la respuesta es "queued" o "running", consulta GET https://vit.doitong.com/api/v1/requests/REQUEST_ID?wait=20 hasta que sea "succeeded"; los archivos están en outputs[].url. POST https://vit.doitong.com/api/v1/quote/flux/dev con el mismo cuerpo devuelve el precio exacto antes de la ejecución.
Esta solicitud: $0.025 25 créditos de API Ejecutar en esta página
Tu nueva clave de API. Solo se muestra ahora y ya está en el código y en el campo de clave de API de abajo. Mantenla en secreto: nunca la incluyas en código que se ejecute en un navegador.
Los controles se generan a partir del esquema del modelo. El código y el precio se actualizan conforme los cambias.
/run/flux/dev?wait=20
cotización → $0.025
# Run the model; wait up to 20 s for the finished result in the same call
curl -X POST "https://vit.doitong.com/api/v1/run/flux/dev?wait=20" \
-H "Authorization: Bearer $DOITONG_API_KEY" \
-H "Content-Type: application/json" \
-d '{
"prompt": "A lighthouse keeper'\''s cottage on a windswept cliff at blue hour, warm light glowing from small square windows, waves breaking far below, long-exposure sea mist, moody cinematic landscape photograph, 35mm, rich detail.",
"num_outputs": 1
}'
# Still "queued" or "running"? Poll with the id from the response:
curl "https://vit.doitong.com/api/v1/requests/REQUEST_ID?wait=20" \
-H "Authorization: Bearer $DOITONG_API_KEY"
const API = "https://vit.doitong.com/api/v1";
const headers = {
"Authorization": "Bearer " + process.env.DOITONG_API_KEY,
"Content-Type": "application/json",
};
// Run the model; wait up to 20 s for the finished result in the same call
let res = await fetch(API + "/run/flux/dev?wait=20", {
method: "POST",
headers,
body: JSON.stringify({
"prompt": "A lighthouse keeper's cottage on a windswept cliff at blue hour, warm light glowing from small square windows, waves breaking far below, long-exposure sea mist, moody cinematic landscape photograph, 35mm, rich detail.",
"num_outputs": 1
}),
});
let request = await res.json();
if (!res.ok) throw new Error(request.error.code + ": " + request.error.message);
// Not finished yet: long-poll until it is
while (request.status === "queued" || request.status === "running") {
res = await fetch(API + "/requests/" + request.id + "?wait=20", { headers });
request = await res.json();
}
if (request.status !== "succeeded") throw new Error(JSON.stringify(request.error));
console.log(request.outputs.map((o) => o.url), request.cost.credits + " API credits ($" + request.cost.usd + ")");
import os
import requests
API = "https://vit.doitong.com/api/v1"
headers = {"Authorization": "Bearer " + os.environ["DOITONG_API_KEY"]}
payload = {
"prompt": "A lighthouse keeper's cottage on a windswept cliff at blue hour, warm light glowing from small square windows, waves breaking far below, long-exposure sea mist, moody cinematic landscape photograph, 35mm, rich detail.",
"num_outputs": 1,
}
# Run the model; wait up to 20 s for the finished result in the same call
r = requests.post(API + "/run/flux/dev", params={"wait": 20}, json=payload, headers=headers)
request = r.json()
if not r.ok:
raise RuntimeError(request["error"]["code"] + ": " + request["error"]["message"])
# Not finished yet: long-poll until it is
while request["status"] in ("queued", "running"):
request = requests.get(API + "/requests/" + request["id"], params={"wait": 20}, headers=headers).json()
if request["status"] != "succeeded":
raise RuntimeError(request["error"])
print([o["url"] for o in request["outputs"]], request["cost"]["credits"], "API credits", "$" + request["cost"]["usd"])
{
"jsonrpc": "2.0",
"id": 1,
"method": "tools/call",
"params": {
"name": "run",
"arguments": {
"model_id": "flux/dev",
"input": {
"prompt": "A lighthouse keeper's cottage on a windswept cliff at blue hour, warm light glowing from small square windows, waves breaking far below, long-exposure sea mist, moody cinematic landscape photograph, 35mm, rich detail.",
"num_outputs": 1
},
"wait_seconds": 20
}
}
}
Tu clave permanece en este navegador: solo se envía a la Doitong API y se guarda en localStorage únicamente si marcas Recordar. Obtener una clave de API
El modelo FLUX de 12 mil millones de parámetros con guía destilada, un programa de muestreo completo (28 pasos de forma predeterminada, ajustable de 1 a 50) y una escala de guía configurable. Sigue prompts detallados y renderiza manos, rostros y texto breve notablemente mejor que la variante schnell, a un precio mayor. Tamaños preestablecidos o personalizados, hasta cuatro imágenes por solicitud, con salida JPEG o PNG.
Envía el nombre canónico o el nombre nativo, no ambos. Nombres canónicos
promptobligatorio
image_sizeopcional
num_inference_stepsopcional
seedopcional
guidance_scaleopcional
num_imagesopcional
num_outputs
output_formatopcional
accelerationopcional
{
"id": "req_8k2m4q7x1z9c3v5b6n0p2r4t",
"object": "request",
"model": "flux/dev",
"status": "succeeded",
"created_at": "2026-10-20T12:00:00Z",
"started_at": "2026-10-20T12:00:02Z",
"completed_at": "2026-10-20T12:00:09Z",
"queue_position": null,
"cost": {
"credits": 25,
"usd": "0.025",
"charged": 25,
"refunded": 0
},
"input": {
"prompt": "A red fox trots through fresh snow at sunrise, soft golden light, shallow depth of field, telephoto lens",
"image_size": "landscape_4_3",
"num_outputs": 1
},
"output": {
"images": [
{
"url": "https://content.doitong.com/dev-api/2026/10/20/req_8k2m4q7x1z9c3v5b6n0p2r4t/0-output.jpeg",
"width": 1024,
"height": 768,
"content_type": "image/jpeg"
}
],
"timings": {},
"seed": 42,
"has_nsfw_concepts": [
false
],
"prompt": "string"
},
"outputs": [
{
"type": "image",
"url": "https://content.doitong.com/dev-api/2026/10/20/req_8k2m4q7x1z9c3v5b6n0p2r4t/0-output.jpeg",
"content_type": "image/jpeg",
"file_size": 1843200,
"width": 1024,
"height": 768,
"stored": true
}
],
"error": null,
"metrics": {
"inference_seconds": 6.8
},
"urls": {
"self": "https://vit.doitong.com/api/v1/requests/req_8k2m4q7x1z9c3v5b6n0p2r4t",
"cancel": "https://vit.doitong.com/api/v1/requests/req_8k2m4q7x1z9c3v5b6n0p2r4t/cancel"
}
}
Para agentes de IA: esta página en Markdown está disponible en https://doitong.com/es-MX/developers/models/flux/dev.md. Todos los modelos y guías: https://doitong.com/developers/llms.txt